{
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    "# CS-109: Fall 2015  -- Lab 11\n",
    "\n",
    "***\n",
    "In today's lab we'll talk about:\n",
    "1. Project Guidelines\n",
    "2. Go through an example project with lots of key elements.\n",
    "3. In the middle of (2.) we'll see Elastic Net regularization as an extension of Lasso. What's a lab without learning?!\n",
    "\n",
    "This is the main file for the lab, `Lab11-2-Data-Processing.ipynb` is supplemental. \n",
    "***\n",
    "\n",
    "## But first, this:\n",
    "You'll be working in groups now, we encourage you to continue to use `git` but **constant communication** is key. [Slack](https://slack.com/) may be a good option for this!\n",
    "\n",
    "[<img src=\"./images/git.png\">](https://xkcd.com/1597/)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Guidelines:\n",
    "Much of this is taken directly from the [Project Page](http://cs109.github.io/2015/pages/projects.html) on the CS109 website.\n",
    "\n",
    "> Goal: to go through the complete data science process to answer questions you have about some topic.\n",
    ">\n",
    "> How?\n",
    "> - acquire the data\n",
    "> - design your visualizations\n",
    "> - run statistical analysis\n",
    "> - communicate the results\n",
    "\n",
    "### Project Milestones:\n",
    "- **Proposal**: Due 11/12/2015\n",
    "- **Proposal Review**: You'll be assigned a TF as an advisor, they'll review your proposal and meet with your **entire** team.\n",
    "\n",
    "\n",
    "### Data science as a process\n",
    "You'll start with an initial idea, your TF should be able to guide you as to the viability of such a proposal. However, as with any project, you'll run into roadblocks! It's important for us to see the entire process and not just the final product. **Your proposal is a great jumping-off point for some of this!**\n",
    "\n",
    ">#### IPython Process Book\n",
    "\n",
    ">An important part of your project is your iPython process book. Your process book details your steps in developing your solution, including how you collected the data, alternative solutions you tried, describing statistical methods you used, and the insights you got. Equally important to your final results is how you got there! Your process book is the place you describe and document the space of possibilities you explored at each step of your project. We strongly advise you to include many visualizations in your process book.Your process book should include the following topics. Depending on your project type the amount of discussion you devote to each of them will vary:\n",
    "\n",
    "> - **Overview and Motivation**: Provide an overview of the project goals and the motivation for it. Consider that this will be read by people who did not see your project proposal.\n",
    "\n",
    "> - **Related Work**: Anything that inspired you, such as a paper, a web site, or something we discussed in class.\n",
    "\n",
    "> - **Initial Questions**: What questions are you trying to answer? How did these questions evolve over the course of the project? What new questions did you consider in the course of your analysis? \n",
    "\n",
    "> - **Data**: Source, scraping method, cleanup, storage, etc.\n",
    "\n",
    "> - **Exploratory Data Analysis**: What visualizations did you use to look at your data in different ways? What are the different statistical methods you considered? Justify the decisions you made, and show any major changes to your ideas. How did you reach these conclusions?\n",
    "\n",
    "> - **Final Analysis**: What did you learn about the data? How did you answer the questions? How can you justify your answers?\n",
    "\n",
    "> - **Presentation**: Present your final results in a compelling and engaging way using text, visualizations, images, and videos on your project web site.\n",
    "\n",
    ">Describe the storytelling elements and goals in your process notebook and show us sketches and screenshots of different web site iterations. As this will be your only chance to describe your project in detail make sure that your process book is a standalone document that fully describes your process and results.\n",
    "\n",
    "### Final Products\n",
    "- **Code**: you've been graded all semester on this, just keep doing a great job at submitting clean code with comments\n",
    "- **Website**: make a *public website* (Github pages, Google sites, etc.), summarize the main results of your project and tell a story, **anyone** should be able to read this part of your project, link to your Process Notebook and Github repo. \n",
    "- **Two-minute presentation**: Each team will create a **two minute** screencas (seriously, 2 minutes) with narration showing a demo of your iPython process book and/or some slides.  Upload to YouTube (or Vimeo) and **embed it into your project web page**. Focus on:\n",
    "    - main contributions rather than on technical details. \n",
    "    - What do you feel is the best part of your project? \n",
    "    - What insights did you gain? \n",
    "    - What is the single most important thing you would like your audience to take away? \n",
    "    - This should be **one of the first things** someone visiting your website should see.\n",
    "\n",
    "### Peer Assessment\n",
    "Remember, data science is an interdisciplinary field, you'll never work alone. It's important that everyone contributes and works well together. So we added this portion to get everyone thinking about teamwork early on.\n",
    "\n",
    "> Peer assessment:\n",
    "> - **Preparation**: were they prepared during team meetings?\n",
    "> - **Contribution**: did they contribute productively to the team discussion and work?\n",
    "> - **Respect for others’ ideas**: did they encourage others to contribute their ideas?\n",
    "> - **Flexibility**: were they flexible when disagreements occurred?\n",
    "\n",
    "### Grading Criteria\n",
    "\n",
    "> - **Project Scope** - Did you choose the appropriate complexity and level of difficulty of your project?\n",
    "> - **Process Book** - Did you follow the data science process and is it well documented in your process book?\n",
    "> - **Solution** - Is your analysis effective and correct in answering your intended questions?\n",
    "> - **Implementation** - What is the quality of your code? Is it appropriately polished, robust, and reliable?\n",
    "> - **Presentation** - Are your web site and screencast clear, engaging, and effective?\n",
    "> - **Peer Evaluations** - Your individual project score will also be influenced by your peer evaluations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Example: Food Inspection Forecasting\n",
    "\n",
    "## **HUGE NOTE:  Much of this is taken from the [food-inspections-evaluation](https://github.com/Chicago/food-inspections-evaluation) repository. It's publicly available, so if you're interested please check it out further.**\n",
    "\n",
    "Some articles talking about the project:\n",
    " - [Washington Post, September 28, 2014](https://www.washingtonpost.com/business/on-it/in-chicago-food-inspectors-are-guided-by-big-data/2014/09/27/96be8c68-44e0-11e4-b47c-f5889e061e5f_story.html)\n",
    " - [Chicago Tribune, July 6, 2015](http://www.chicagotribune.com/business/ct-chicago-allstate-inspections-0703-biz-20150706-story.html)\n",
    "\n",
    "Let's check out the website they built for their project (built with Gihub pages):\n",
    "\n",
    "[<img src=\"./images/chicago_page.png\">](http://chicago.github.io/food-inspections-evaluation/)\n",
    "\n",
    "We're looking for some of the major elements we discussed above. \n",
    "\n",
    "Some questions about the webpage:\n",
    "\n",
    "- Is the writing directed appropriately?\n",
    "- Motivation?\n",
    "- Do they link to their sources?\n",
    "\n",
    "Goal: \n",
    "> To forecast food establishments that are most likely to have critical violations so that they may be inspected first.\n",
    "\n",
    "Main Result:\n",
    "> <img src=\"./images/main_result.png\">\n",
    "\n",
    "Data: \n",
    "> https://data.cityofchicago.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download the Data:\n",
    "\n",
    "The entire analysis was done in R, I used their code to processed the data to the point where I could use it easily. See `Data-Processing.ipynb`. This is an ipython notebook that uses `R-magic`. If you don't know `R` you can just download the files with processed data into your lab directory.\n",
    "\n",
    "**OR, just follow along without running the code**. Running the code isn't as important for this lab.\n",
    "\n",
    "Download the following files (right-click -> \"save file as\"): (1-2) are important for running the model below, (1) is just for data exploration.\n",
    "1. [Model Matrix](https://www.dropbox.com/s/t90lh34kuyt2byf/model_matrix.csv?dl=0)\n",
    "2. [Outcome](https://www.dropbox.com/s/nj8jtuqisk74qj6/TARGET.csv)\n",
    "3. [Food Inspections data (huge)](https://www.dropbox.com/s/6x1dcin3viwyumy/food_inspections.csv?dl=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# special IPython command to prepare the notebook for matplotlib\n",
    "%matplotlib inline \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")\n",
    "\n",
    "# special matplotlib argument for improved plots\n",
    "from matplotlib import rcParams\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(97432, 17)\n"
     ]
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    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Inspection_ID</th>\n",
       "      <th>DBA_Name</th>\n",
       "      <th>AKA_Name</th>\n",
       "      <th>License</th>\n",
       "      <th>Facility_Type</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Inspection_Date</th>\n",
       "      <th>Inspection_Type</th>\n",
       "      <th>Results</th>\n",
       "      <th>Violations</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44247</td>\n",
       "      <td>EAT A PITA</td>\n",
       "      <td>EAT A PITA</td>\n",
       "      <td>1222441</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Risk 1 (High)</td>\n",
       "      <td>3155 N HALSTED ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>Complaint</td>\n",
       "      <td>Pass</td>\n",
       "      <td>30. FOOD IN ORIGINAL CONTAINER, PROPERLY LABEL...</td>\n",
       "      <td>41.939441</td>\n",
       "      <td>-87.649103</td>\n",
       "      <td>(41.93944115701468, -87.64910277148812)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44248</td>\n",
       "      <td>LA GONDOLA</td>\n",
       "      <td>LA GONDOLA</td>\n",
       "      <td>1336561</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Risk 1 (High)</td>\n",
       "      <td>2914 N ASHLAND AVE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>2010-01-21</td>\n",
       "      <td>Canvass</td>\n",
       "      <td>Pass</td>\n",
       "      <td>32. FOOD AND NON-FOOD CONTACT SURFACES PROPERL...</td>\n",
       "      <td>41.934679</td>\n",
       "      <td>-87.668625</td>\n",
       "      <td>(41.93467889167928, -87.66862496860755)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44249</td>\n",
       "      <td>Azha Restaurant Inc.</td>\n",
       "      <td>Azha Restaurant</td>\n",
       "      <td>1334073</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Risk 1 (High)</td>\n",
       "      <td>960 W BELMONT AVE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>2010-01-21</td>\n",
       "      <td>Canvass Re-Inspection</td>\n",
       "      <td>Pass</td>\n",
       "      <td>18. NO EVIDENCE OF RODENT OR INSECT OUTER OPEN...</td>\n",
       "      <td>41.940027</td>\n",
       "      <td>-87.653811</td>\n",
       "      <td>(41.94002687624888, -87.65381102535457)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44250</td>\n",
       "      <td>SAINT JOSEPH HOSPITAL</td>\n",
       "      <td>SAINT JOSEPH HOSPITAL</td>\n",
       "      <td>1144381</td>\n",
       "      <td>Hospital</td>\n",
       "      <td>Risk 1 (High)</td>\n",
       "      <td>2900 N LAKE SHORE DR</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>Canvass</td>\n",
       "      <td>Pass</td>\n",
       "      <td>33. FOOD AND NON-FOOD CONTACT EQUIPMENT UTENSI...</td>\n",
       "      <td>41.934403</td>\n",
       "      <td>-87.636806</td>\n",
       "      <td>(41.93440298707838, -87.63680583708285)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44251</td>\n",
       "      <td>SAINT JOSEPH HOSPITAL</td>\n",
       "      <td>SAINT JOSEPH HOSPITAL</td>\n",
       "      <td>1144380</td>\n",
       "      <td>Hospital</td>\n",
       "      <td>Risk 1 (High)</td>\n",
       "      <td>2900 N LAKE SHORE DR</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>Canvass</td>\n",
       "      <td>Pass</td>\n",
       "      <td>33. FOOD AND NON-FOOD CONTACT EQUIPMENT UTENSI...</td>\n",
       "      <td>41.934403</td>\n",
       "      <td>-87.636806</td>\n",
       "      <td>(41.93440298707838, -87.63680583708285)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Inspection_ID               DBA_Name               AKA_Name  License  \\\n",
       "0          44247             EAT A PITA             EAT A PITA  1222441   \n",
       "1          44248             LA GONDOLA             LA GONDOLA  1336561   \n",
       "2          44249   Azha Restaurant Inc.        Azha Restaurant  1334073   \n",
       "3          44250  SAINT JOSEPH HOSPITAL  SAINT JOSEPH HOSPITAL  1144381   \n",
       "4          44251  SAINT JOSEPH HOSPITAL  SAINT JOSEPH HOSPITAL  1144380   \n",
       "\n",
       "  Facility_Type           Risk                Address     City State    Zip  \\\n",
       "0    Restaurant  Risk 1 (High)     3155 N HALSTED ST   CHICAGO    IL  60657   \n",
       "1    Restaurant  Risk 1 (High)    2914 N ASHLAND AVE   CHICAGO    IL  60657   \n",
       "2    Restaurant  Risk 1 (High)     960 W BELMONT AVE   CHICAGO    IL  60657   \n",
       "3      Hospital  Risk 1 (High)  2900 N LAKE SHORE DR   CHICAGO    IL  60657   \n",
       "4      Hospital  Risk 1 (High)  2900 N LAKE SHORE DR   CHICAGO    IL  60657   \n",
       "\n",
       "  Inspection_Date        Inspection_Type Results  \\\n",
       "0      2010-01-05              Complaint    Pass   \n",
       "1      2010-01-21                Canvass    Pass   \n",
       "2      2010-01-21  Canvass Re-Inspection    Pass   \n",
       "3      2010-02-09                Canvass    Pass   \n",
       "4      2010-02-09                Canvass    Pass   \n",
       "\n",
       "                                          Violations   Latitude  Longitude  \\\n",
       "0  30. FOOD IN ORIGINAL CONTAINER, PROPERLY LABEL...  41.939441 -87.649103   \n",
       "1  32. FOOD AND NON-FOOD CONTACT SURFACES PROPERL...  41.934679 -87.668625   \n",
       "2  18. NO EVIDENCE OF RODENT OR INSECT OUTER OPEN...  41.940027 -87.653811   \n",
       "3  33. FOOD AND NON-FOOD CONTACT EQUIPMENT UTENSI...  41.934403 -87.636806   \n",
       "4  33. FOOD AND NON-FOOD CONTACT EQUIPMENT UTENSI...  41.934403 -87.636806   \n",
       "\n",
       "                                  Location  \n",
       "0  (41.93944115701468, -87.64910277148812)  \n",
       "1  (41.93467889167928, -87.66862496860755)  \n",
       "2  (41.94002687624888, -87.65381102535457)  \n",
       "3  (41.93440298707838, -87.63680583708285)  \n",
       "4  (41.93440298707838, -87.63680583708285)  "
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food = pd.read_csv('food_inspections.csv', sep=',')\n",
    "\n",
    "print food.shape\n",
    "food.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Focus in on 2014 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.5 s, sys: 61.2 ms, total: 4.56 s\n",
      "Wall time: 4.52 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import dateutil.parser\n",
    "food['year'] = [dateutil.parser.parse(x).year for x in food.Inspection_Date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "food2014 = food.loc[food['year'].values == 2014,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20797, 19)\n"
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       "      <th></th>\n",
       "      <th>Inspection_ID</th>\n",
       "      <th>DBA_Name</th>\n",
       "      <th>AKA_Name</th>\n",
       "      <th>License</th>\n",
       "      <th>Facility_Type</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Zip</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10557</th>\n",
       "      <td>274810</td>\n",
       "      <td>COLD STONE CREAMERY</td>\n",
       "      <td>COLD STONE CREAMERY</td>\n",
       "      <td>1488799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk 3 (Low)</td>\n",
       "      <td>3510 N HALSTED ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>2014-08-14</td>\n",
       "      <td>Canvass</td>\n",
       "      <td>Out of Business</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.945777</td>\n",
       "      <td>-87.649588</td>\n",
       "      <td>(41.94577730075354, -87.64958777668471)</td>\n",
       "      <td>2014</td>\n",
       "      <td>set([8])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41023</th>\n",
       "      <td>920444</td>\n",
       "      <td>GALLISTEL LANGUAGE ACADEMY</td>\n",
       "      <td>GALLISTEL LANGUAGE ACADEMY</td>\n",
       "      <td>2046708</td>\n",
       "      <td>School</td>\n",
       "      <td>Risk 1 (High)</td>\n",
       "      <td>10200 S AVENUE J</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60617</td>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Canvass</td>\n",
       "      <td>Fail</td>\n",
       "      <td>29. PREVIOUS MINOR VIOLATION(S) CORRECTED 7-42...</td>\n",
       "      <td>41.709957</td>\n",
       "      <td>-87.534223</td>\n",
       "      <td>(41.70995714781606, -87.5342225149751)</td>\n",
       "      <td>2014</td>\n",
       "      <td>set([1])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41024</th>\n",
       "      <td>920445</td>\n",
       "      <td>SUBWAY</td>\n",
       "      <td>SUBWAY</td>\n",
       "      <td>2183563</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Risk 1 (High)</td>\n",
       "      <td>4025 E 106TH ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60617</td>\n",
       "      <td>2014-02-03</td>\n",
       "      <td>Canvass</td>\n",
       "      <td>Pass w/ Conditions</td>\n",
       "      <td>2. FACILITIES TO MAINTAIN PROPER TEMPERATURE -...</td>\n",
       "      <td>41.702577</td>\n",
       "      <td>-87.525872</td>\n",
       "      <td>(41.70257660937248, -87.52587169711877)</td>\n",
       "      <td>2014</td>\n",
       "      <td>set([2])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41025</th>\n",
       "      <td>920446</td>\n",
       "      <td>SHARKS FISH &amp; CHICKEN</td>\n",
       "      <td>SHARKS FISH &amp; CHICKEN</td>\n",
       "      <td>1477112</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Risk 2 (Medium)</td>\n",
       "      <td>2027 E 95TH ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60617</td>\n",
       "      <td>2014-02-03</td>\n",
       "      <td>Canvass</td>\n",
       "      <td>Pass</td>\n",
       "      <td>38. VENTILATION: ROOMS AND EQUIPMENT VENTED AS...</td>\n",
       "      <td>41.722372</td>\n",
       "      <td>-87.574275</td>\n",
       "      <td>(41.72237211196818, -87.57427469548966)</td>\n",
       "      <td>2014</td>\n",
       "      <td>set([2])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41026</th>\n",
       "      <td>920447</td>\n",
       "      <td>EL GUERO</td>\n",
       "      <td>EL GUERO</td>\n",
       "      <td>18978</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Risk 1 (High)</td>\n",
       "      <td>9027-9029 S COMMERCIAL AVE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60617</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>Canvass Re-Inspection</td>\n",
       "      <td>Fail</td>\n",
       "      <td>33. FOOD AND NON-FOOD CONTACT EQUIPMENT UTENSI...</td>\n",
       "      <td>41.731076</td>\n",
       "      <td>-87.551134</td>\n",
       "      <td>(41.7310758719025, -87.55113416594888)</td>\n",
       "      <td>2014</td>\n",
       "      <td>set([2])</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Inspection_ID                    DBA_Name                    AKA_Name  \\\n",
       "10557         274810         COLD STONE CREAMERY         COLD STONE CREAMERY   \n",
       "41023         920444  GALLISTEL LANGUAGE ACADEMY  GALLISTEL LANGUAGE ACADEMY   \n",
       "41024         920445                      SUBWAY                      SUBWAY   \n",
       "41025         920446       SHARKS FISH & CHICKEN       SHARKS FISH & CHICKEN   \n",
       "41026         920447                    EL GUERO                    EL GUERO   \n",
       "\n",
       "       License  Facility_Type             Risk                      Address  \\\n",
       "10557  1488799            NaN     Risk 3 (Low)           3510 N HALSTED ST    \n",
       "41023  2046708         School    Risk 1 (High)            10200 S AVENUE J    \n",
       "41024  2183563     Restaurant    Risk 1 (High)             4025 E 106TH ST    \n",
       "41025  1477112     Restaurant  Risk 2 (Medium)              2027 E 95TH ST    \n",
       "41026    18978  Grocery Store    Risk 1 (High)  9027-9029 S COMMERCIAL AVE    \n",
       "\n",
       "          City State    Zip Inspection_Date        Inspection_Type  \\\n",
       "10557  CHICAGO    IL  60657      2014-08-14                Canvass   \n",
       "41023  CHICAGO    IL  60617      2014-01-15                Canvass   \n",
       "41024  CHICAGO    IL  60617      2014-02-03                Canvass   \n",
       "41025  CHICAGO    IL  60617      2014-02-03                Canvass   \n",
       "41026  CHICAGO    IL  60617      2014-02-26  Canvass Re-Inspection   \n",
       "\n",
       "                  Results                                         Violations  \\\n",
       "10557     Out of Business                                                NaN   \n",
       "41023                Fail  29. PREVIOUS MINOR VIOLATION(S) CORRECTED 7-42...   \n",
       "41024  Pass w/ Conditions  2. FACILITIES TO MAINTAIN PROPER TEMPERATURE -...   \n",
       "41025                Pass  38. VENTILATION: ROOMS AND EQUIPMENT VENTED AS...   \n",
       "41026                Fail  33. FOOD AND NON-FOOD CONTACT EQUIPMENT UTENSI...   \n",
       "\n",
       "        Latitude  Longitude                                 Location  year  \\\n",
       "10557  41.945777 -87.649588  (41.94577730075354, -87.64958777668471)  2014   \n",
       "41023  41.709957 -87.534223   (41.70995714781606, -87.5342225149751)  2014   \n",
       "41024  41.702577 -87.525872  (41.70257660937248, -87.52587169711877)  2014   \n",
       "41025  41.722372 -87.574275  (41.72237211196818, -87.57427469548966)  2014   \n",
       "41026  41.731076 -87.551134   (41.7310758719025, -87.55113416594888)  2014   \n",
       "\n",
       "          month  \n",
       "10557  set([8])  \n",
       "41023  set([1])  \n",
       "41024  set([2])  \n",
       "41025  set([2])  \n",
       "41026  set([2])  "
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print food2014.shape\n",
    "food2014.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A breakdown of the inspection types in 2014:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Canvass                                   12229\n",
       "License                                    2418\n",
       "Canvass Re-Inspection                      2284\n",
       "Complaint                                  1653\n",
       "License Re-Inspection                       708\n",
       "Complaint Re-Inspection                     689\n",
       "Short Form Complaint                        644\n",
       "Consultation                                 67\n",
       "Suspected Food Poisoning                     45\n",
       "Recent Inspection                            30\n",
       "Suspected Food Poisoning Re-inspection       17\n",
       "Non-Inspection                                2\n",
       "Special Events (Festivals)                    2\n",
       "License-Task Force                            2\n",
       "Tag Removal                                   2\n",
       "Complaint-Fire                                2\n",
       "Short Form Fire-Complaint                     1\n",
       "Not Ready                                     1\n",
       "KITCHEN CLOSED FOR RENOVATION                 1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food2014.Inspection_Type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A summary of the risk types appears below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Risk 1 (High)      14742\n",
       "Risk 2 (Medium)     4416\n",
       "Risk 3 (Low)        1630\n",
       "All                    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food2014.Risk.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building\n",
    "### Goal of the model\n",
    "> The principle question is whether we can reasonably determine the probability that a restaurant inspection will yield at least one critical violation.\n",
    "\n",
    "Clearly this goal was refined with some experience handling this data, you'll have the same experience as you work with your data. \n",
    "\n",
    "\n",
    "\n",
    "Variable Name (Literal)                       | Variable Description\n",
    "----------------------------------------------|---------------------------------\n",
    "`Inspectorblue`                               | Indicator variable for Sanitarian Cluster 1\n",
    "`Inspectorbrown`                              | Indicator variable for Sanitarian Cluster 2\n",
    "`Inspectorgreen`                              | Indicator variable for Sanitarian Cluster 3\n",
    "`Inspectororange`                             | Indicator variable for Sanitarian Cluster 4\n",
    "`Inspectorpurple`                             | Indicator variable for Sanitarian Cluster 5\n",
    "`Inspectoryellow`                             | Indicator variable for Sanitarian Cluster 6\n",
    "`pastCritical`                                | Indicates any previous critical violations (last visit)\n",
    "`pastSerious`                                 | Indicates any previous serious violations (last visit)\n",
    "`timeSinceLast`                               | Elapsed time since previous inspection\n",
    "`ageAtInspection`                             | Age of business license at the time of inspection\n",
    "`consumption_on_premises_incidental_activity` | Presence of a license for consumption / incidental activity\n",
    "`tobacco_retail_over_counter`                 | Presence of an additional license for tobacco sales\n",
    "`temperatureMax`                              | The daily high temperature on the day of inspection\n",
    "`heat_burglary`                               | Local intensity of recent burglaries\n",
    "`heat_sanitation`                             | Local intensity of recent sanitation complaints\n",
    "`heat_garbage`                                | Local intensity of recent garbage cart requests\n",
    "\n",
    "\n",
    "\n",
    "## Elastic Net Regularized Lostic Regression\n",
    "\n",
    "#### Recall: Logistic Regression with Lasso\n",
    "We've seen Logistic regression with the Lasso penalty, a form of regularization and variable selection. As a reminder, the Logistic regression with Lasso minimizes the equation:\n",
    "\n",
    "$$\n",
    "\\min_{(\\beta_0, \\beta) \\in \\mathbb{R}^{p+1}} -\\left[\\frac{1}{N} \\sum_{i=1}^N y_i \\cdot (\\beta_0 + x_i^T \\beta) - \\log (1+e^{(\\beta_0+x_i^T \\beta)})\\right] + \\alpha\\ ||\\beta||_1\n",
    "$$\n",
    "\n",
    "We're essentially looking for solutions $(\\beta_0, \\beta)$ that are **small** in terms of the $L_1$ norm. We've also seen Ridge regression, another type of regularization, essentially the same but with $\\lambda\\ ||\\beta||_2^2$ instead of the $L_1$ norm. \n",
    "\n",
    "\n",
    "####  Elastic Net Regularized Logistic Regression\n",
    "For the final model they decided to use Elastic Net regularization on a logistic regression. This is essentially a combination of Lasso and Ridge regularization, you can see it below in the formulation. \n",
    "\n",
    ">$$\n",
    "\\begin{aligned}\n",
    "\\log = \\frac{\\text{Pr}(V=1|X=x)}{\\text{Pr}(V=0|X=x)} = \\beta_0 + \\beta^T x\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    ">Thus, the objective function is to minimize \n",
    ">\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\min_{(\\beta_0, \\beta) \\in \\mathbb{R}^{p+1}} -\\left[\\frac{1}{N} \\sum_{i=1}^N y_i \\cdot (\\beta_0 + x_i^T \\beta) - \\log (1+e^{(\\beta_0+x_i^T \\beta)})\\right] + \\alpha (1-\\lambda)||\\beta||_2^2/2 + \\alpha\\lambda||\\beta||_1\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "In our code $\\lambda =$ `l1_ratio`, and an $\\alpha$ range is determined by `eps`, see the [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html) for more details. \n",
    "\n",
    "**Note: They didn't justify their use of this model. We know that prediction is their end-goal. I would like to know what other methods they tried or if they have a specific reason for using this model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mm = pd.read_csv('model_matrix.csv', sep=',')\n",
    "X = mm.values.astype(np.double)\n",
    "y = pd.read_csv('TARGET.csv', sep=',').values.astype(np.double).transpose()[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import lasso_path, enet_path\n",
    "eps = 5e-6  # the smaller it is the longer is the path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing regularization path using the lasso...\n",
      "Computing regularization path using the elastic net...\n",
      "CPU times: user 15 ms, sys: 2.59 ms, total: 17.6 ms\n",
      "Wall time: 17 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print(\"Computing regularization path using the lasso...\")\n",
    "alphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps, fit_intercept=False)\n",
    "\n",
    "print(\"Computing regularization path using the elastic net...\")\n",
    "alphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8, fit_intercept=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x134f1b3d0>"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rMYQQERER2VCN6jpKy66gvCoDNbWFqK4tQI2qEFPHfgyFvbtJX5FIhPyiRNSo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\nEM3q/wEUWsuxspr4KgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134e98c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Color Palette, seaborn has the best colors i.m.o.\n",
    "col = sns.color_palette(\"husl\", 16)\n",
    "\n",
    "# make the lasso path\n",
    "plt.figure(1)\n",
    "ax = plt.gca()\n",
    "ax.set_color_cycle(col)\n",
    "l1 = plt.plot(-np.log10(alphas_lasso), coefs_lasso.T)\n",
    "l2 = plt.plot(-np.log10(alphas_enet), coefs_enet.T, linestyle='--')\n",
    "plt.xlim([1, 4])\n",
    "\n",
    "plt.xlabel('-log(alpha)')\n",
    "plt.ylabel('coefficients')\n",
    "plt.title('Lasso and Elastic-Net Paths')\n",
    "plt.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'), loc='lower left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# subset variables with large coefficients\n",
    "# selected somewhat arbitrarily, could do something better here.\n",
    "\n",
    "# show all coefficients\n",
    "# keep = np.ones(16, dtype=bool)\n",
    "# show only if sum of coefficients larger than something\n",
    "# keep = np.abs(coefs_enet.T.sum(axis = 0)) > 0.5\n",
    "# show only if least shrunk coefficient larger than something\n",
    "keep = np.abs(coefs_enet.T[99,:]) > 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x146795710>"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EMAoJRoQQQgghhBBGIcGIEEIIIYQQwigkGBEALFmyhIYNGxq7GqUWHh7OpEmT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lKpFPPmzQNg0aJFFBcX87WvfY2XXnqJxYsXH17BIjLsbGyecLyXXT7Vmo4Pdw4rEhkg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14670cf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Color Palette, seaborn has the best colors i.m.o.\n",
    "col = sns.color_palette(\"hls\", keep.sum())\n",
    "\n",
    "plt.figure(1)\n",
    "ax = plt.gca()\n",
    "ax.set_color_cycle(col)\n",
    "l2 = plt.plot(-np.log10(alphas_enet), coefs_enet.T[:,keep])\n",
    "plt.xlim([1, 4])\n",
    "\n",
    "plt.xlabel('-log(alpha)')\n",
    "plt.ylabel('coefficients')\n",
    "plt.title('Elastic-Net Paths (largest coefficients)')\n",
    "plt.legend(l2, list(mm.columns.values[keep]), loc='upper left')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Path Charts\n",
    "Both Lasso and Elastic Net regularization have the property that with enough regularization (large $\\alpha$) (or small $-log(\\alpha)$ on the chart) the coefficients ($\\hat\\beta_0, \\hat\\beta_1, ..., \\hat\\beta_p$) get shrunk to 0. That means they're no longer playing a role in the prediction! Recall:\n",
    "\n",
    "$$\\hat y_i = \\frac{exp(\\hat\\beta_0 + \\hat\\beta_1 X_{i1} + ... + \\hat\\beta_p X_{ip})}{1 + exp(\\hat\\beta_0 + \\hat\\beta_1 X_{i1} + ... + \\hat\\beta_p X_{ip})}$$\n",
    "\n",
    "First let's notice that the `Inspector*` variables are all present, except for one -- `InspectorYellow` is missing, but it's easy to see that it wouldn't be informative at all. Simply knowing that the region wasn't any of (`blue`, `brown`, ..., `purple`) will tell you that the inspection was in `yellow`. So the regularization does a good job at getting rid of this redundant information. \n",
    "\n",
    "What this means for us is that we can look at a chart like the one above and see that (from left to right) the first variable to enter the model is `InspectorBlue`, `InspectorPurple`, etc. \n",
    "\n",
    "Other main findings they gathered from this chart:\n",
    "\n",
    "> - Establishments that had previous critical or serious violations\n",
    "> - Whether the establishment has a tobacco license or has an incidental alcohol consumption license\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Validation/testing\n",
    "The super impressive thing about this analysis (to me) is their way of testing their results. \n",
    "\n",
    "The discovery of a critical violation is a time-sensitive issue! The earlier an inspector can find these critical violations the better. So instead of just looking at things like accuracy, f1 score, etc. they looked for a way of vallidating the model within the contect of the problem. Here's how:\n",
    "\n",
    "- They used the data before **July 31, 2014** to fit the model. \n",
    "- They selected a model parameters using **August 1-31, 2014** as a tuning dataset.\n",
    "- They used the model to predict critical violations in the 1,637 food establishments that were scheduled for **September 1, 2014 to October 31, 2014**. \n",
    "- Based on the predictions they could create a schedule of inspections that would hopefully yield a higher number of critical violations earlier.\n",
    "- They then let the inspectors go on about their business, reposting critical violations as they do.\n",
    "- At the end of the month, they compared what happened to what `could have happened` if they used the schedule based on predictions.\n",
    "\n",
    "> After formulating the analytical model, the the principal question for researchers turned to whether this analytical model provides more efficiency for the food inspection team. CDPH operational procedures requires the department to inspect every risk 1 and risk 2 restaurant. Therefore, the operational goal is to allow inspectors to discover critical violations earlier than their current operations (business-as-usual)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results:\n",
    "## Taken from [Report](https://github.com/Chicago/food-inspections-evaluation/blob/master/REPORTS/forecasting-restaurants-with-critical-violations-in-Chicago.pdf)\n",
    "<img src=\"./images/days.png\">\n",
    "<img src=\"./images/cumulative1.png\">\n",
    "<img src=\"./images/efficiency.png\">\n",
    "<img src=\"./images/cumulative2.png\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Some further notes:\n",
    "\n",
    "- Further inspection of their code revealed a very detailed analysis, the selected their tuning parameters using a validation set. \n",
    "- Due to the ongoing nature of the problem, it makes sense to validate on only the latest data.\n",
    "- Further exploration in terms of models could show some improvement.\n",
    "- The writeup contained lots more analysis and information than the website for those who are interested.\n",
    "\n",
    "# MORE PROJECTS:\n",
    "You'll see some previous projects that we thought were also exceptional:\n",
    "http://cs109.github.io/2014/pages/projects.html\n",
    "\n",
    "- Ale Augur: Quantifying and Predicting Beer Preference with the Untapped API by Ian Nightingale, Alexander Jaffe and Brian Mendel ([Website](http://beer.iandnightingale.com/), [Screencast](https://www.youtube.com/watch?v=J6Svwwetaj8))\n",
    "- ClimbRec by Amy Skerry ([Website](http://mindhive.mit.edu/saxe/cs109proj/), [Screencast](https://www.youtube.com/watch?v=41BG2z9SbMg&feature=youtu.be))\n",
    "- The Green Canvas by Ahmed Hosny, Jili Huang and Yingyi Wang ([Website](http://ahmedhosny.github.io/theGreenCanvas/), [Screencast](http://vimeo.com/114379373))\n",
    "- Predicting Citation Counts of arXiv Papers by Marion Dierickx, George Miller, Sam Sinai and Stephen Portillo ([Website](https://sites.google.com/site/teamcitations/), [Screencast](http://youtu.be/bjAcHu8JRG0))\n",
    "- Improving University Energy Efficiency: Building Energy Demand Prediction by Wen Xie, Wette Constant and Bin Yan ([Website](http://cs109-energy.github.io/), [Screencast](http://youtu.be/JiAQxctOntQ))\n",
    "- Predicting AirBnb Success by Enrique Dominguez-Meneses, Jameson Rogers, Noah Taylor and Muhammed Yildiz ([Website](http://hamelsmu.github.io/AirbnbScrape/), [Screencast](https://www.youtube.com/watch?v=raGjUj5qArc))\n",
    "\n",
    "Some of the screencasts are down, but the webpages are still up!"
   ]
  }
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